Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis

Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, the parameters of a PLSA model are trained using the Expectation Maximization (EM) algorithm, and as a result, the trained model is dependent on the initialization values so that performance can be highly variable. In this paper we present a method for using LSA analysis to initialize a PLSA model. We also investigated the performance of our method for the tasks of text segmentation and retrieval on personal-size corpora, and present results demonstrating the efficacy of our proposed approach.

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Ayman Farahat, Francine Chen
• Keywords: EACL 2006, Latent Semantic Analysis, Natural Language Processing, PLSA Model, Probabilistic Latent Semantic Analysis